Using the LeiCNS-PK3.0 Physiologically-Based Pharmacokinetic Model to Predict Brain Extracellular Fluid Pharmacokinetics in Mice

Introduction The unbound brain extracelullar fluid (brain ECF ) to plasma steady state partition coefficient, K p,uu,BBB , values provide steady-state information on the extent of blood-brain barrier (BBB) transport equilibration, but not on pharmacokinetic (PK) profiles seen by the brain targets. Mouse models are frequently used to study brain PK, but this information cannot directly be used to inform on human brain PK, given the different CNS physiology of mouse and human. Physiologically based PK (PBPK) models are useful to translate PK information across species. Aim Use the LeiCNS-PK3.0 PBPK model, to predict brain extracellular fluid PK in mice. Methods Information on mouse brain physiology was collected from literature. All available connected data on unbound plasma, brain ECF PK of 10 drugs (cyclophosphamide, quinidine, erlotonib, phenobarbital, colchicine, ribociclib, topotecan, cefradroxil, prexasertib, and methotrexate) from different mouse strains were used. Dosing regimen dependent plasma PK was modelled, and Kpuu,BBB values were estimated, and provided as input into the LeiCNS-PK3.0 model to result in prediction of PK profiles in brain ECF . Results Overall, the model gave an adequate prediction of the brain ECF PK profile for 7 out of the 10 drugs. For 7 drugs, the predicted versus observed brain ECF data was within two-fold error limit and the other 2 drugs were within five-fold error limit. Conclusion The current version of the mouse LeiCNS-PK3.0 model seems to reasonably predict available information on brain ECF from healthy mice for most drugs. This brings the translation between mouse and human brain PK one step further.